Ge Y, Fitzpatrick J M, Votaw J R, Gadamsetty S, Maciunas R J, Kessler R M, Margolin R A
Department of Psychiatry, Vanderbilt University, Nashville, Tennessee.
J Comput Assist Tomogr. 1994 Sep-Oct;18(5):800-10. doi: 10.1097/00004728-199409000-00021.
We present a validation study of an algorithm for retrospective registration of PET and MR brain images.
This algorithm involves two steps. In the first step, the two volumes are reformatted by aligning their interhemispheric fissure planes (midsagittal plane). In the second step, the corresponding planes parallel to the midsagittal plane are further aligned in the reformatted volumes to produce a 3D rigid body registration of the two original volumes. It is an efficient algorithm because both steps are performed in 2D spaces, and in each step only a small number of landmarks are required. A user-friendly system has been implemented to facilitate easy and fast processing of registration and reformatting of image volumes. The accuracy of this algorithm is validated using clinical scans of neurosurgical patients with a stereotaxic frame attached to their skull. The frame-based stereotaxic system provides an effective method for transforming image coordinates from different image volumes into a common coordinate system. This common coordinate system is used for assessing the spatial correspondence of each pixel in the registered image volumes. Validation using the stereotaxic image volumes enables objective estimation of retrospective registration accuracy.
Analysis of 11 MR/PET image pairs indicates that our registration method not only is efficient but also provides adequate accuracy for most clinical evaluation of PET studies.
We have implemented and validated an efficient algorithm for retrospective registration of PET and MR brain images.
我们展示了一种用于PET和MR脑图像回顾性配准算法的验证研究。
该算法包括两个步骤。第一步,通过对齐两半球间裂平面(正中矢状平面)对两个容积进行重新格式化。第二步,在重新格式化的容积中进一步对齐与正中矢状平面平行的相应平面,以生成两个原始容积的三维刚体配准。这是一种高效的算法,因为两个步骤均在二维空间中执行,且在每个步骤中仅需要少量地标。已实现了一个用户友好的系统,以方便对图像容积进行配准和重新格式化的轻松快速处理。使用附有立体定向框架的神经外科患者的临床扫描来验证该算法的准确性。基于框架的立体定向系统提供了一种将不同图像容积的图像坐标转换为公共坐标系的有效方法。这个公共坐标系用于评估配准图像容积中每个像素的空间对应关系。使用立体定向图像容积进行验证能够客观估计回顾性配准的准确性。
对11对MR/PET图像的分析表明,我们的配准方法不仅高效,而且对于PET研究的大多数临床评估而言提供了足够的准确性。
我们已经实现并验证了一种用于PET和MR脑图像回顾性配准的高效算法。